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A consensus prognostic gene expression classifier for ER positive breast cancer.

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Teschendorff, Andrew E 
Naderi, Ali 
Barbosa-Morais, Nuno L 
Pinder, Sarah E 
Ellis, Ian O 


BACKGROUND: A consensus prognostic gene expression classifier is still elusive in heterogeneous diseases such as breast cancer. RESULTS: Here we perform a combined analysis of three major breast cancer microarray data sets to hone in on a universally valid prognostic molecular classifier in estrogen receptor (ER) positive tumors. Using a recently developed robust measure of prognostic separation, we further validate the prognostic classifier in three external independent cohorts, confirming the validity of our molecular classifier in a total of 877 ER positive samples. Furthermore, we find that molecular classifiers may not outperform classical prognostic indices but that they can be used in hybrid molecular-pathological classification schemes to improve prognostic separation. CONCLUSION: The prognostic molecular classifier presented here is the first to be valid in over 877 ER positive breast cancer samples and across three different microarray platforms. Larger multi-institutional studies will be needed to fully determine the added prognostic value of molecular classifiers when combined with standard prognostic factors.



Breast Neoplasms, Cohort Studies, Female, Gene Expression Profiling, Genetic Markers, Humans, Oligonucleotide Array Sequence Analysis, Prognosis, Receptors, Estrogen, Reproducibility of Results

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Genome Biol

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Springer Science and Business Media LLC